OnLine Handwriting Recognition
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1 OnLine Handwriting Recognition (Master Course of HTR) Alejandro H. Toselli Departamento de Sistemas Informáticos y Computación Universidad Politécnica de Valencia February 26, 2008 A.H. Toselli (ITI - UPV) OnLine Handwriting Recognition 1 / 34
2 Outline 1 Introduction 2 Feature Extraction for On-Line HTR 3 DFT - Data Global Modelling 4 DFT - Data Local Modelling 5 Experimental Results A.H. Toselli (ITI - UPV) OnLine Handwriting Recognition 2 / 34
3 Introduction On-Line Handwriting Recognition Context Acquisition types: Video 01 Escáner Tabla Digitalizadora ON LINE OFF LINE Cámara Usual applications: Sign recognition and verification. Recognition of postal codes. Recognition of courtesy amount in bank checks. Transcription of ancient documents. A.H. Toselli (ITI - UPV) OnLine Handwriting Recognition 3 / 34
4 Motivations Introduction Time-based Feature Extraction adequate for using with HMM. Because of the similarity existing between the nature of on-line HTR and Speech recognition. Tamil HCR Competition. A.H. Toselli (ITI - UPV) OnLine Handwriting Recognition 4 / 34
5 Introduction Tamil HCR Competition - Motivation Isolated character samples of 156 Tamil characters. Written by native Tamil writers. Off-Line and On-Line corpus version. Some examples of Tamil s symbols: User 54 User 111 Evaluation criterion is the highest top-choice accuracy at zero reject rate labelled samples available for training purposes unlabelled samples available for testing purposes. A.H. Toselli (ITI - UPV) OnLine Handwriting Recognition 5 / 34
6 Introduction On-Line Recognition Representation On-Line handwriting is represented by a points sequence in R 2 space ordered in time. Parametric curve. A isolated handwritten character/word: A series of consecutive pen-downs and pen-up strokes. Each stroke is a sequence of coordinates (x t, y t ) t = 0..N 1. Some on-line devices included the pressure made on each point. A.H. Toselli (ITI - UPV) OnLine Handwriting Recognition 6 / 34
7 Feature Extraction for On-Line HTR Our Traditional Feature Extraction Seven Feature Extractions in function of the time: x t and y t coordinates normalized between 0 and. x t and y t first derivatives normalized between 0 and 1. x t and y t second derivatives. All derivatives are calculated using: curvature: f t = C c=1 (r t+c r t c ) 2 r C t {x t, y t,x c=1 c2 t, y t } k t = x t y t x t y t ( ) x t 2 + y t 3/2 2 A.H. Toselli (ITI - UPV) OnLine Handwriting Recognition 7 / 34
8 Feature Extraction for On-Line HTR DFT representation On-Line handwriting can be represented by a sequence of coordinates (x t, y t ) t = 0..N 1. also as a function f t : t (x t + iy t ) C, or f t : t (x t, y t ) R 2. Pair of Discrete Fourier Transforms: N 1 Direct Transform F n = f t e 2πi tn N Inverse Transform f t = 1 N t=0 N 1 n=0 F n e 2πi tn N A.H. Toselli (ITI - UPV) OnLine Handwriting Recognition 8 / 34
9 Feature Extraction for On-Line HTR Global/Local Data Modelling DFT Feature Extraction can be applied to: the whole character stroke - Global Data Modelling local stroke segments - Local Data Modelling We use two different classifiers: K-NN classifier for global data modelling. Continuous density HMMs for local data modelling. A.H. Toselli (ITI - UPV) OnLine Handwriting Recognition 9 / 34
10 DFT - Data Global Modelling Introduction - DFT Modelling Example Time Coord X Coord Y Time Amplitude 0 Frequency A.H. Toselli (ITI - UPV) OnLine Handwriting Recognition 10 / 34
11 DFT - Data Global Modelling Data Global Modelling - Direct DFT DFT feature extraction examples using the first 40 coefficients: Amplitude Amplitude 0 Amplitude 0 Amplitude 0 Amplitude 0 Frequency Frequency Frequency Frequency Frequency Amplitude Amplitude 0 Amplitude 0 Amplitude 0 Amplitude 10 Frequency Frequency Frequency Frequency Frequency A.H. Toselli (ITI - UPV) OnLine Handwriting Recognition 11 / 34
12 DFT - Data Global Modelling Data Global Modelling - Inverse DFT Examples of reconstructed samples considering different number of Fourier-Coefficients. Original 2 FC 4 FC 6 FC 12 FC 20 FC 30 FC 60 FC A.H. Toselli (ITI - UPV) OnLine Handwriting Recognition 12 / 34
13 DFT - Data Local Modelling Local Data Modelling Local DFT Features Extraction: Sliding-Window running through the whole stroke. Window size: 40 points. Moving step: 1 points. Hamming windows. A.H. Toselli (ITI - UPV) OnLine Handwriting Recognition 13 / 34
14 Demo 1 DFT - Data Local Modelling Exit from the Demo A.H. Toselli (ITI - UPV) OnLine Handwriting Recognition 14 / 34
15 Demo 1 DFT - Data Local Modelling Exit from the Demo A.H. Toselli (ITI - UPV) OnLine Handwriting Recognition 15 / 34
16 Demo 1 DFT - Data Local Modelling Exit from the Demo A.H. Toselli (ITI - UPV) OnLine Handwriting Recognition 16 / 34
17 Demo 1 DFT - Data Local Modelling Exit from the Demo A.H. Toselli (ITI - UPV) OnLine Handwriting Recognition 17 / 34
18 Demo 1 DFT - Data Local Modelling Exit from the Demo A.H. Toselli (ITI - UPV) OnLine Handwriting Recognition 18 / 34
19 Demo 1 DFT - Data Local Modelling Exit from the Demo A.H. Toselli (ITI - UPV) OnLine Handwriting Recognition 19 / 34
20 Demo 1 DFT - Data Local Modelling Exit from the Demo A.H. Toselli (ITI - UPV) OnLine Handwriting Recognition 20 / 34
21 Demo 1 DFT - Data Local Modelling Exit from the Demo A.H. Toselli (ITI - UPV) OnLine Handwriting Recognition 21 / 34
22 Demo 1 DFT - Data Local Modelling Exit from the Demo A.H. Toselli (ITI - UPV) OnLine Handwriting Recognition 22 / 34
23 Demo 1 DFT - Data Local Modelling Exit from the Demo A.H. Toselli (ITI - UPV) OnLine Handwriting Recognition 23 / 34
24 DFT - Data Local Modelling Sliding-Window Segments Org-Sign Spectrum Rbt-Sign 0 Org-Sign Spectrum Rbt-Sign A.H. Toselli (ITI - UPV) OnLine Handwriting Recognition 24 / 34
25 DFT - Data Local Modelling Assembling of Regenerated Stroke Segments Original Signal Reconstructed from 4 Fourier-Coefficients Sliding-Window, 40 points windows-size and 20 points moving-step. A.H. Toselli (ITI - UPV) OnLine Handwriting Recognition 25 / 34
26 Experimental Results Tamil Corpus Partition Isolated character samples of 156 Tamil characters. Written by native Tamil writers. Experiments carried out with 2 different versions of the corpus. Full version: Reduced version: Training Test Total #Writers #Samples Training Test Total #Writers #Samples A.H. Toselli (ITI - UPV) OnLine Handwriting Recognition 26 / 34
27 Experimental Results Classification Results with K-NN - Global Modelling Used feature extraction: First 20 Fourier coefficients. Coefficient are represented by real and imaginary parts. Experiments carried our with the full version of the corpus. K-NN WER(%) A.H. Toselli (ITI - UPV) OnLine Handwriting Recognition 27 / 34
28 Experimental Results HMM Classifier - Local Modelling HMM classifier specification: Left-to-Right topology. Number of states as a function of average class length. Mixture of Gaussian densities as probability emission per HMM state. Baum-Welch re-estimation algorithm for training process. Viterbi algorithm for test recognition for process. A.H. Toselli (ITI - UPV) OnLine Handwriting Recognition 28 / 34
29 Experimental Results DFT Application Problems Inconvenient: DFT applied to a complex function modulated by a Hamming filter. As alternatives, DFT was applied: on the angle function, defined as: a t = arctan y t. x t on x t and on y t in an independent way. Original Angle-Based Reconstruction A.H. Toselli (ITI - UPV) OnLine Handwriting Recognition 29 / 34
30 Experimental Results Results with Different DFT Application Schemes Experiments carried out with the reduced version of the corpus and using only DFT coefficients as features. #Gauss DFT-Complex DFT-Ang DFT-XYind A.H. Toselli (ITI - UPV) OnLine Handwriting Recognition 30 / 34
31 Experimental Results Experimental Validation of the Hamming Filter Experiments carried out with the reduced version of the corpus and using only DFT coefficients as features. #Gauss Hamming Rectangular A.H. Toselli (ITI - UPV) OnLine Handwriting Recognition 31 / 34
32 Experimental Results Classification Results with HMMs - Full Version Feature extraction: TFE: x and y normalized coordinates, their first and second derivatives, and curvature. FFC: First 4 Fourier transform coefficients (real and imaginary parts). #Gauss TFE FFC TFE+FFC A.H. Toselli (ITI - UPV) OnLine Handwriting Recognition 32 / 34
33 Experimental Results Tamil HCR Competition - Ranked List of Results Rank Recognition Accuracy Data-Entry on on on on on on on on off off off on off off off on on A.H. Toselli (ITI - UPV) OnLine Handwriting Recognition 33 / 34
34 Experimental Results Conclusions and Future Work DFT feature extraction complements time-based feature extraction. Experiments applying DFT on x t and on y t in an independent way. Test the feature extraction with the On-Line UNIPEN corpus. A.H. Toselli (ITI - UPV) OnLine Handwriting Recognition 34 / 34
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